The population across Northern Europe is aging. Coupled with socio-economic challenges, health care systems are at risk of overloading and incurring unsustainable high costs. Rehabilitation services are used disproportionately by older people. One solution pertinent to rural areas is to change the model of rehabilitation to incorporate new technologies. This has the potential to free resources and reduce costs. However, implementation is challenging. In the Northern Periphery and Artic Programme (NPA), the Smart sensor Devices for rehabilitation and Connected health (SENDoc) project [1] is focused on introducing wearable sensor systems among elderly communities to support their rehabilitation. It is important to understand the context into which change is introduced. Therefore, an overview of the current state of health care systems in the four partner countries is presented, defining the concept of rehabilitation and how remote rehabilitation is currently delivered. Advantages (e.g. enhanced outcomes, less cost and enhanced patient engagement), and disadvantages of remote rehabilitation (e.g. complexity involved in the use of technology, design and safety issues) are discussed. It is concluded that the key advantage of remote rehabilitation is the potential to support change in patient behaviour, empowering active participation and living independently, with less need to travel for face-to-face sessions. Remote rehabilitation can make enhance quality of health care service delivery.However, all relevant stakeholders including medical staff and patients should be included in the design of the technology employed with a focus on simplicity, usability and robustness. Compliance with Security and the new GDPR regulation will be key to supporting remote rehabilitation. In addition, the diversity of available platforms and devices must also be supported to ensure interoperability. Finally, remote rehabilitation needs to be further validated in practice. Attempts to implement and sustain change should be cognisant of local and current organization of health care and of existing enablers and barriers.
The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings.
Background The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services. Objective This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. Methods Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants’ intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device. Results The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants’ age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%. Conclusions This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.
Biomechanical analysis of gait is commonly used in physiotherapy. Ground reaction forces during phases of gait is one element of kinetic analysis. In this article, we analyze if the MoveSole® smart insole is valid and accurate equipment for measuring ground reaction forces in clinical physiotherapy. MoveSole® StepLab is a mobile measurement system for instant underfoot force measurements during gait. Unique electromagnetic film (EMFI) based sensor technology and printed electronics production technology is integrated in the MoveSole® StepLab measurement system. The MoveSole® StepLab measures plantar ground reaction force distribution over the sensors and provides an estimation of the maximum total ground reaction force. We developed a two phase validation process to extract relevant parameters and compared the results to a Kistler force plate using the BioWare® analyzing program as a reference method. Our results show that MoveSole® smart insoles reach the strong level of accuracy needed in clinical work concerning highest ground reaction forces during step (Pearson correlation .822 - .875). The correlation of the time when the maximum ground reaction force occurred was moderate, e.g. during heel strike or toe-off (Pearson correlation natural gait speed .351 - .462, maximum gait speed .430). Our conclusion is that MoveSole® smart insoles are a potential tool for analyzing and monitoring gait ground reaction forces during physiotherapy processes.
Background: As the European population ages, it becomes increasingly important to promote and facilitate healthy and active ageing and age-friendly societies. Professionals across a range of disciplines and sectors need knowledge and skills to support both. Objective: This scoping review aims to identify and map the literature on learning needs, learning outcomes and respective curricula in healthy and active ageing and age-friendly society concepts. Inclusion criteria: Studies focused on the teaching/learning process in healthy and active ageing and/or age-friendly society, of any design type, are eligible. Included studies may focus on undergraduate, postgraduate or continuing education and on any aspect of the educational process, such as needs analysis, content delivery, learner satisfaction/acceptability, or education outcome. Methods: This review will follow the Joanna Briggs Institute (JBI) methodology for conducting scoping reviews. Four electronic databases, PubMed, EBSCO (Academic Search Complete), Scopus and Applied Social Sciences Index and Abstracts (ASSIA), will be searched, limited to studies published from 1st January 2000. Text language will be limited to English, German, Greek, Portuguese, Finnish, and Slovenian. Google Scholar and Research Gate will be searched for grey literature, limited to the first 50 results of each. Title and abstract screening, followed by full-text screening will be undertaken independently by at least two reviewers. The JBI extraction tool will be adapted for data extraction. Quality assessment will be conducted using a tool developed by Hawker and colleagues. A narrative synthesis will outline the data in relation to the aims and objectives outlined.
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